{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Embedding, Flatten, LeakyReLU, Merge\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of the data:\n",
    "\n",
    "- instant: record index\n",
    "- dteday : date\n",
    "- season : season (1:springer, 2:summer, 3:fall, 4:winter)\n",
    "- yr : year (0: 2011, 1:2012)\n",
    "- mnth : month ( 1 to 12)\n",
    "- hr : hour (0 to 23)\n",
    "- holiday : weather day is holiday or not (extracted from [Web Link])\n",
    "- weekday : day of the week\n",
    "- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.\n",
    "+ weathersit : \n",
    "  - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "  - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "  - 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds\n",
    "  - 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog\n",
    "- temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)\n",
    "- atemp: Normalized feeling temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-16, t_max=+50 (only in hourly scale)\n",
    "- hum: Normalized humidity. The values are divided to 100 (max)\n",
    "- windspeed: Normalized wind speed. The values are divided to 67 (max)\n",
    "- casual: count of casual users\n",
    "- registered: count of registered users\n",
    "- cnt: count of total rental bikes including both casual and registered\n",
    "\n",
    "## Functions to Note:\n",
    "- LeakyRelu activation\n",
    "- Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LeakyReLU?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/pandas/core/ops.py:533: PerformanceWarning: Adding/subtracting array of DateOffsets to Series not vectorized\n",
      "  \"Series not vectorized\", PerformanceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1.293840e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1.293844e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1.293847e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1.293851e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1.293854e+18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit  temp   hum  \\\n",
       "0       1     1   0        0        6           0           1  0.24  0.81   \n",
       "1       1     1   1        0        6           0           1  0.22  0.80   \n",
       "2       1     1   2        0        6           0           1  0.22  0.80   \n",
       "3       1     1   3        0        6           0           1  0.24  0.75   \n",
       "4       1     1   4        0        6           0           1  0.24  0.75   \n",
       "\n",
       "   windspeed  cnt                time             t  \n",
       "0        0.0   16 2011-01-01 00:00:00  1.293840e+18  \n",
       "1        0.0   40 2011-01-01 01:00:00  1.293844e+18  \n",
       "2        0.0   32 2011-01-01 02:00:00  1.293847e+18  \n",
       "3        0.0   13 2011-01-01 03:00:00  1.293851e+18  \n",
       "4        0.0    1 2011-01-01 04:00:00  1.293854e+18  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rides = pd.read_csv('hour.csv')\n",
    "rides['time'] = pd.to_datetime(rides['dteday'])+pd.Series([pd.DateOffset(hours = a ) for a in rides.hr.tolist()])\n",
    "rides.drop(['instant','yr','dteday','casual','registered', 'atemp'],1,inplace=True)\n",
    "rides['t'] = rides.time.values.astype(float)\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot data for the first 10 days:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PEkKOEUIeIYTc0MsnnwVmFmsAgH3To1istVb13E5eP5fR7JmtL81xNKdr0Ei8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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106ec74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(rides.time[:240],rides.cnt[:240])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>mnth</th>\n",
       "      <th>hr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>time</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>2011-01-01 00:00:00</td>\n",
       "      <td>1.293840e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>2011-01-01 01:00:00</td>\n",
       "      <td>1.293844e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32</td>\n",
       "      <td>2011-01-01 02:00:00</td>\n",
       "      <td>1.293847e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2011-01-01 03:00:00</td>\n",
       "      <td>1.293851e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01 04:00:00</td>\n",
       "      <td>1.293854e+18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  mnth  hr  holiday  weekday  workingday  weathersit  temp   hum  \\\n",
       "0       1     1   0        0        6           0           1  0.24  0.81   \n",
       "1       1     1   1        0        6           0           1  0.22  0.80   \n",
       "2       1     1   2        0        6           0           1  0.22  0.80   \n",
       "3       1     1   3        0        6           0           1  0.24  0.75   \n",
       "4       1     1   4        0        6           0           1  0.24  0.75   \n",
       "\n",
       "   windspeed  cnt                time             t  \n",
       "0        0.0   16 2011-01-01 00:00:00  1.293840e+18  \n",
       "1        0.0   40 2011-01-01 01:00:00  1.293844e+18  \n",
       "2        0.0   32 2011-01-01 02:00:00  1.293847e+18  \n",
       "3        0.0   13 2011-01-01 03:00:00  1.293851e+18  \n",
       "4        0.0    1 2011-01-01 04:00:00  1.293854e+18  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quant_features = ['temp', 'hum', 'windspeed','t']\n",
    "x_scaler = StandardScaler()\n",
    "y_scaler = StandardScaler()\n",
    "# TODO:\n",
    "# standardise the quant features AND the y variable:\n",
    "x_scaler.fit_transform?\n",
    "\n",
    "rides.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat_features = ['season', 'hr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']\n",
    "\n",
    "# Preprocess data so that it starts from 0 and not 1\n",
    "for feature in cat_features:\n",
    "    if rides[feature].min() > 0:\n",
    "        rides[feature] -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(20,input_dim = 10,activation= 'relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Embedding?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Flatten?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create the submodels for the categorical and quantitative models separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:17: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n"
     ]
    }
   ],
   "source": [
    "# Embed the models\n",
    "cat_models = []\n",
    "for feature in cat_features:\n",
    "    # TODO: embed each categorical feature.\n",
    "    # start each model with getting a Sequential class\n",
    "    # Use nunique on the pandas dataframe to get unique values in each category\n",
    "    # In embedding remember to set input_length = 1\n",
    "    # Finally `Flatten()` the Embedding (to lose the time factor)\n",
    "    model_cat = \n",
    "    \n",
    "    cat_models.append(model_cat)\n",
    "    \n",
    "cat_model = Sequential()\n",
    "cat_model.add(Merge(cat_models)) # Merge (and sum these models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dense?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Todo: Get a submodel for \n",
    "# Start a Sequential() class\n",
    "# Add a fully connected hidden layer with a number of units of your choosing\n",
    "# Add LeakyRelu() as the activation\n",
    "quant_model = \n",
    "\n",
    "quant_model.add(LeakyReLU())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: UserWarning: The `Merge` layer is deprecated and will be removed after 08/2017. Use instead layers from `keras.layers.merge`, e.g. `add`, `concatenate`, etc.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Merge([cat_model, quant_model], mode='concat')) # Concat the two seperate submodels into one\n",
    "# Todo: Add any number of layers with activation function LeakyRelu() (similar to above)\n",
    "model.compile(optimizer='adadelta', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "merge_13 (Merge)             (None, 33)                0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 15)                510       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_9 (LeakyReLU)    (None, 15)                0         \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 16        \n",
      "=================================================================\n",
      "Total params: 1,128.0\n",
      "Trainable params: 1,128.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Look at the number of parameters\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the last 21 days \n",
    "test_data = rides[-21*24:]\n",
    "train_data = rides[:-21*24]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.8576     \n",
      "Epoch 2/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.5655     \n",
      "Epoch 3/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.3414     \n",
      "Epoch 4/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2933     \n",
      "Epoch 5/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2790     \n",
      "Epoch 6/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2706     \n",
      "Epoch 7/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2644     \n",
      "Epoch 8/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2592     \n",
      "Epoch 9/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2557     \n",
      "Epoch 10/10\n",
      "16875/16875 [==============================] - 0s - loss: 0.2520     \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x11f47aac8>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: The way that the columns go into train/test x has to be the same as how it was defined in the model\n",
    "train_x = [train_data[col].values for col in cat_features+[quant_features]]\n",
    "test_x = [test_data[col].values for col in cat_features+[quant_features]]\n",
    "\n",
    "# Train the model\n",
    "model.fit(train_x, train_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RCAQCoVmUIckegMsBfERKeRmAHjJrBQBAKtNspaeTlPJjUso9Uso9O3furPKnM0GVLN9N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\nSdb7M43CvdIRcGMWj6kkalRQkocLzSwfL1e414SSLOLsfli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kwEldII1x8ShA16HoVpI8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yQcFQ/wqlCYoPmca5KbMMn9jgXbNPClqcW1kbpyi0SAZKHj3KZzIBIDaZ3cwlTkFgAfYBoM3NLS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\n/j9Y38HejsedJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ddab160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_y = model.predict(test_x, batch_size=256)\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.plot(test_data.time, y_scaler.inverse_transform(test_data.cnt), label='Actual')\n",
    "plt.plot(test_data.time, y_scaler.inverse_transform(pred_y), label='Prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try and beat my score of 0.36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "256/504 [==============>...............] - ETA: 0s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.36286450851531254"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_x, test_data['cnt'], batch_size=256)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "latex_envs": {
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 0
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
